Subido por Carlos Meza


Methods for mapping
3D chromosome architecture
Rieke Kempfer
* and Ana Pombo
Abstract | Determining how chromosomes are positioned and folded within the nucleus is
critical to understanding the role of chromatin topology in gene regulation. Several methods are
available for studying chromosome architecture, each with different strengths and limitations.
Established imaging approaches and proximity ligation-​based chromosome conformation capture
(3C) techniques (such as DNA-​FISH and Hi-​C, respectively) have revealed the existence of
chromosome territories, functional nuclear landmarks (such as splicing speckles and the nuclear
lamina) and topologically associating domains. Improvements to these methods and the recent
development of ligation-​free approaches, including GAM, SPRITE and ChIA-​Drop, are now helping
to uncover new aspects of 3D genome topology that confirm the nucleus to be a complex, highly
organized organelle.
Chromosome territories
The nuclear volumes occupied
by each specific chromosome.
Chromosomes tend to interact
predominantly within
themselves and occupy distinct
regions within the interphase
Chromosomes fold into distinct
subcompartments, which
correlate with transcriptional
activity (A compartment) or
repression (B compartment).
The A and B compartments
are defined by Hi-​C contact
Epigenetic Regulation and
Chromatin Architecture
Group, Berlin Institute for
Medical Systems Biology,
Max-Delbrück Centre for
Molecular Medicine,
Berlin, Germany.
Institute for Biology,
Humboldt University of
Berlin, Berlin, Germany.
*e-​mail: [email protected]
mdc-​; [email protected]
The nucleus of human cells harbours 46 densely packed
chromosomes. Chromosomes are folded into hierarchical domains at different genomic scales, which
likely enable efficient packaging and organize the
genome into functional compartments. Chromosomes
occupy distinct positions within the nucleus, called
chromosome territories , which are partitioned into
chromosomal compartments , and further partitioned
into topologically associating domains (TADs) and
chromatin loops mediated by CCCTC-​binding factor (CTCF)
or enhancer–promoter contacts (Fig. 1). Chromatin folding is a major feature of gene regulation and dynamically
changes in development and disease1–4. Transcriptional
control is mediated through physical contacts between
enhancers and target genes, which occur via loop formation between the respective DNA elements. Functional
loops between regulatory regions and genes are thought
to occur predominantly within TADs. The expression
of genes can also be influenced by their positioning
relative to spatial landmarks inside the nucleus that are
enriched for specific biochemical activities, such as the
nuclear lamina. The disruption of enhancer–gene contacts and alteration of nuclear subcompartments play
important roles in disease, including congenital disorders and cancer. Importantly, many disease-​associated
mutations of the linear genomic sequence can only be
understood by considering their 3D conformation in
nuclear space.
Advances in our understanding of chromosome folding have been restricted by a lack of approaches that can
map chromatin contacts genome-​wide while simultaneously retrieving spatial information, such as molecular
distances between different genomic regions or between
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genomic regions and distinct nuclear compartments.
Until recently, studies of 3D genome folding were limited to two main technologies: imaging, particularly
fluorescence in situ hybridization of DNA (DNA-​FISH); and
approaches based on chromosome conformation capture
(3C), namely Hi-​C (high-​throughput chromosome conformation capture). DNA-​FISH was a revolutionary
approach, which allowed visualization of the spatial
organization of chromosomes and genes in the nucleus5,6.
The approach provides single-​cell information, but
typically has a limited throughput that allows only a
small number of genomic loci to be analysed at a time.
3C-based approaches, which depend on proximity ligation
of DNA ends involved in a chromatin contact, have
helped identify enhancer–promoter contacts. High-​
throughput derivatives, such as Hi-​C, map chromatin
contacts genome-​wide at a length scale of hundreds of
kilobases to a few megabases.
More recently, improvements in imaging techniques
have increased the number of loci that can be analysed in
parallel7 and have extended the approach to live cells8,9.
Orthogonal ligation-​free approaches have also emerged,
namely genome architecture mapping (GAM)10, split-​
pool recognition of interactions by tag extension (SPRITE)11
and chromatin-​interaction analysis via droplet-​based and
barcode-​linked sequencing (ChIA-​Drop)12, which have
started to reveal novel aspects of chromatin organization. GAM, SPRITE and ChIA-​Drop map chromatin
contacts genome-​wide and identify topological domains
but also robustly detect a previously unappreciated level
of high-​complexity chromatin contacts that involve three
or more DNA fragments and uncover specific contacts
that span tens of megabases.
Chromatin feature
Chromosome territories
~100 Mb
DNA Chr 2 Chr 9
Hubs and compartments
A compartment
A/B compartment
Gene factory
B compartment
1–100 Mb
Chr 11
TADs and loop domains
Distance (nm)
Nuclear lamina
Chr 11
40 kb
3 Mb
replicating replicating
Chr 21
Chr 6
Chr 6
28 Mb
30 Mb
49 Mb
Live-cell imaging
Promoter–enhancer contacts
54 Mb
<1 kb
few Mb
54 Mb
840 kb
49 Mb
Contact probability
Chr 3
Shh gene Enhancer
Here, we review the main approaches currently
used in 3D genome research, highlighting their major
advantages and caveats. To recognize the strengths
of each technique, it is important to understand the
principles and experimental details underlying each
34.3 Mb
35 Mb
method, their intrinsic biases and their power to
capture specific aspects of 3D genome architecture
(Table 1) . We discuss major features of 3D genome
organization that have emerged, at the kilobase scale
and above, through the application of these different
◀ Fig. 1 | Methods for studying the major features of 3D chromatin folding across
different genomic scales. a | Chromosomes occupy discrete territories in the nucleus,
which were first detected using imaging techniques. The 3D-​fluorescence in situ
hybridization (3D-​FISH) image shows the positions of the chromosome territories of
chromosome 2 (red) and chromosome 9 (green) within DAPI-​stained nuclei (blue)
from mouse embryonic stem cells (ESCs). Chromosome territories are also detected
as regions of high-​frequency intrachromosomal interactions on contact maps
generated by chromosome conformation capture (3C)-based methods (such as Hi-​C
(high-​throughput chromosome conformation capture)) and ligation-​free approaches
(such as genome architecture mapping (GAM)). b | DNA inside the nucleus separates
into hubs of active (A compartment) and inactive (B compartment) chromatin, clustering
around the nucleolus, splicing speckles, transcription factories and other nuclear bodies
not represented here. Electron spectroscopy imaging of the mouse epiblast shows
the distribution of heterochromatin (yellow) around the nucleolus (light blue) and at the
nuclear periphery. Decondensed euchromatin (dark blue) is positioned more centrally in
the nucleus. Nucleic acid-​based structures are stained yellow, protein-​based structures
blue. Hi-​C and split-​pool recognition of interactions by tag extension (SPRITE) contact maps
of mouse chromosome 11 show the separation of chromatin into discrete contact hubs
(A and B compartments), which are visible as checkerboard-​like contact patterns.
c | At shorter genomic length scales, chromatin folds into topologically associating
domains (TADs), which overlap with domains of early and late replication, and DNA
loops, that arise from cohesin-​mediated interactions between paired CCCTC-​binding
factor (CTCF) proteins. Multiplexed FISH of consecutive DNA segments in a 2-Mb region
in the human genome shows the emergence of TADs in the population-​average distance
map. In Hi-​C and GAM contact maps, TADs are represented by regions of high internal
interaction frequencies and demarcated by a drop in local interactions at their
boundaries. d | Contacts between a gene and its cis-​regulatory elements occur via loop
formation between the enhancer bound by RNA polymerase II (Pol II) and the gene
promoter. These contacts can be detected by live-​cell imaging; shown are contacts
between the enhancer (green) and promoter (blue) of the eve gene in a Drosophila
melanogaster embryo, with simultaneous imaging of eve mRNA expression (red).
The circular chromosome conformation capture (4C)-sequencing track shows the
interactions between the Shh gene promoter and the ZRS (a limb-​specific enhancer
of the Shh gene) in the anterior forelimb in mice. GAM data can be processed using the
mathematic model statistical inference of co-​segregation (SLICE) to extract the most
significant enhancer–promoter contacts from the data set, resulting in a contact matrix
with only the high-​probability interactions10. The most significant interaction at the Sox2
locus can be found between the Sox2 gene and one of its well-​studied enhancers189.
For parts a, b and c: HiGlass190 was used to generate contact maps for previously published
Hi-​C data from mouse ESCs191; heat maps for GAM and SPRITE were generated from
normalized published matrix files from previously published mouse ESC data (for GAM10,
for SPRITE11). LAD, lamina-​associated domain; rRNA, ribosomal RNA. 3D-​FISH image
reprinted from ref.187, CC BY 2.0 ( Electron
spectroscopy image reprinted from ref.188, CC BY 3.0 (
licenses/by/3.0/). Part c reprinted with permission from ref.32, Science. Part d adapted from
ref.85, Springer Nature Limited, and from ref.48, CC BY 3.0 (
Topologically associating
(TADs). Chromosomal regions
that fold into self-​associating
domains, with high internal
interaction frequencies,
demarcated by a clear drop
of local interactions with
neighbouring regions at
their boundaries.
Chromatin loops
Local regions of high
interaction frequency between
two genomic loci indicate that
these regions form the basis of
a DNA loop. Loops often form
between regions with divergent
CCCTC-​binding factor (CTCF)
sites, or between enhancers
and their target promoters.
technologies, and highlight discrepancies between
approaches. We will not cover chromatin folding at
the level of nucleosomes, which has been reviewed
Imaging-​based detection of contacts
The visualization of nuclear structures and specific
genomic sequences is key to understanding how chromatin is organized in the nucleus. Various light micro­
scopy and electron microscopy techniques can be used
to identify nuclear compartments or image the physical
positions of specific genomic loci in the nucleus of fixed
or live cells. The most commonly used imaging technique for detecting chromatin contacts in fixed cells is
DNA-​FISH. Contacts can be visualized in live cells using
insertions of DNA binding site arrays (such as the
Lac operator-​repressor14,15, Tet operator-​repressor16
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and ANCHOR 17 systems) or, more recently, using
CRISPR-​based approaches (Fig. 2).
Measuring contacts with DNA-​FISH. FISH uses fluorescently tagged DNA sequences (such as oligonucleotides)
as probes to hybridize to complementary target regions
of interest in the genome (Fig. 2). For hybridization to
occur, a single-​stranded probe needs to be able to enter
the nucleus, which is usually achieved by permeabilizing the cell with a detergent or organic solvent, such as
methanol. To ensure the probe can bind to its target, the
DNA is most often denatured by heat and formamide
treatment. The genomic regions highlighted by the
hybridized fluorescent probes are then visualized by
DNA-​FISH is typically used to measure the physical
distances between two or a few differentially labelled
genomic regions of interest. A chromatin contact is
often defined by a distance threshold, which is usually set arbitrarily according to the scale of genomic
distances between the regions of interest and the resolution of the microscope. Thus, chromatin contacts
have been inferred when fluorescent signals co-​localize
within a spatial distance of 50 nm to 1 µm (refs18–21),
although it is not clear whether distances at the top end
of this range (which are close to the diameter of a whole
chromosome) represent true interactions or indirect
non-​random positioning. DNA-​FISH can also be used
to visualize chromatin compaction22 or positioning of
genomic regions with respect to nuclear structures,
such as the nuclear lamina23. The overall distributions
of spatial distances between loci or relative to the nuclear
periphery found across the cell population are usually
summarized by the frequency of co-​localization (that
is, the frequency with which chromatin contacts are
detected in the cell population), but other metrics, such
as mean or median distances, are also used. The data
are compared with the physical distances between other
(control) genomic regions (which are often separated by
similar genomic distances to the experimental loci) or, in
some cases, with the nuclear diameter or volume. These
metrics can help distinguish specific chromosomal conformations but can also be ambiguous, depending on
the choice of control probes or if allelic differences or
other forms of heterogeneity are present within the cell
The accuracy and power to detect different nuclear
structures or contacts also depend on how well the
organization of the target DNA and nuclear compartments is preserved during the FISH procedure, on the
resolution of the microscope and on the size of the target
genomic sequence. FISH experiments use probes made
of a collection of small DNA fragments that are either
synthesized (oligos) or produced by nick-translation
from larger DNA molecules (plasmids, fosmids, bacterial artificial chromosomes or whole mammalian
chromosomes), resulting in overlapping fragments of
100–500 bp. The probes often cover genomic sequences
ranging in length from 30 kb up to entire chromosomes.
The signal-​to-noise ratio for locus detection increases
with the target length due to increased local fluorescence
and higher target specificity. Thus, with standard 3D
Table 1 | comparison of methods used to detect chromatin contacts
number of
contacts per
Multiplicity single-​cell number of cells
of contacts information
Detectable Protocol
3C-​based methods
Proximity ligation and selection of
target regions with primers, detection
by quantitative PCR
One versus
100 million192
Proximity ligation and enrichment
for contacts with one bait region by
inverse PCR, detection by sequencing
One versus all Pairwise
Robust: 10 million193,
low input: 340,000
Proximity ligation and enrichment
for larger target region with primers,
detection by sequencing
Many versus
Robust: 50–70
million195, low input: 2 mediated
Proximity ligation and enrichment for
all ligated contact pairs, detection
by sequencing
All versus all
Robust: 2–5
million64, low input:
Tethered proximity ligation and
enrichment for all ligated contact
pairs, detection by sequencing
All versus all
25 million57
Proximity ligation and pull-​down of
specific protein-​mediated contacts,
detection by sequencing
Many versus
Robust: 100 million198, Protein-​
low input: 500,000
Capture-​C, Proximity ligation and target
enrichment using probes for genomic
regions of interest, detection by
Many versus
Robust: 100,000
All versus all
Proximity ligation and enrichment for
all ligated contact pairs, detection
by sequencing
(ref.199), low input:
10,000–20,000 (ref.97)
Fixation to flatten cells, hybridization Between
of fluorescent probes to target regions, 2 and 52
measurement of 2D spatial distances
Pairwise or
All in spatial
Fixation of cells, hybridization of
fluorescent probes for target regions,
measurement of 3D spatial distances
2 and 52
Pairwise or
All in spatial
Fixation of cells, cryosectioning,
hybridization of fluorescent probes
for target regions, measurement of 2D
spatial distances
2 and 52
Pairwise or
All in spatial
Fluorescent labelling of genomic loci
in living cells, measurement of spatial
distances over time
2 and 12
Pairwise or
All in spatial
Pairwise or
All in spatial
Ligation-​free methods
Cryosectioning of fixed cells, DNA
All versus all
extraction from nuclear sections and
sequencing, inferring spatial distances
from co-​segregation of genomic
regions in nuclear sections
Fixation of cells, identification of
crosslinked chromatin fragments by
split-​pool barcoding and sequencing
All versus all
10 million11
Fixation of cells, identification of
crosslinked chromatin fragments by
droplet-​based and barcode-​linked
All versus all
10 million12
3C, chromosome conformation capture; 4C, circular chromosome conformation capture; 5C, chromosome conformation capture carbon copy ; ChIA-​Drop,
chromatin-​interaction analysis via droplet-​based and barcode-​linked sequencing; ChIA-​PET, chromatin interaction analysis by paired-​end tag sequencing;
C-​HiC, capture HiC; FISH, fluorescence in situ hybridization; GAM, genome architecture mapping; Hi-​C, high-​throughput chromosome conformation capture;
PL AC-​seq, proximity ligation-​assisted chromatin immunoprecipitation sequencing; SPRITE, split-​pool recognition of interactions by tag extension; TCC, tethered
chromosome capture. aClassical FISH experiments rarely distinguish between more than 2–5 differentially labelled regions simultaneously204.
Cycles of probe hybridization can increase this number up to 52 (ref.205).
b CRISPR-based live-cell imaging
Chemical fixation, permeabilization,
DNA denaturation
living cell
Target region
c Image analysis
~150 nm
Measure spatial distances
Fig. 2 | imaging-​based approaches to visualize chromatin contacts.
a | Fluorescence in situ hybridization of DNA (DNA-FISH) uses fluorescently
labelled probes that hybridize to specific genomic loci in the nucleus.
Typically , cells are fixed and permeabilized and, upon denaturing of the
DNA, FISH probes hybridize to their complementary target region (yellow
circle). The FISH procedure can be performed in whole cells, embryos, thick
tissue slices (3D-​FISH) or thin cryosections of cells (cryo-​FISH). The nucleolus
is represented in pink. b | CRISPR-​based live-​cell imaging can be performed
in the intact, living cell. Typically , dead Cas9 (dCas9) is fused to green
CCCTC-​binding factor
(CTCF). A transcription factor
with 11 conserved zinc-​finger
(ZF) domains. This nuclear
protein is able to use different
combinations of the ZF
domains to bind different DNA
target sequences and proteins.
CTCF is enriched at
topologically associating
domain (TAD) borders, where
its binding can be important to
specify TAD border definition.
The combination of DNA, RNA
and protein that constitutes the
chromosomes in eukaryotic
cells. Broadly, heterochromatin
is associated with transcriptional
repression and euchromatin is
associated with transcriptional
fluorescent protein (GFP) and the fusion protein is recruited to the target
region by small guide RNAs (sgRNAs), which are complementary to
the region of interest. c | For all of the techniques, chromatin contacts are
assessed by the spatial distances between the fluorophores targeting the
regions of interest (yellow and red circles). To determine the specificity of a
contact, spatial distances between interacting loci should be compared with
distances between non-​interacting loci in numerous cells. The distribution
of distances, the mean distance and the median distance can all inform
about the quality and abundance of the contact in a cell population.
FISH long-​range contacts within large genomic regions,
such as between TADs10,24 or in whole chromosomes25,
can be accurately detected. However, short-​range interactions between chromosomal regions that are less than
100 kb apart are difficult to detect, making it harder to
quantify fine-​scale chromatin folding below the TAD
level, such as enhancer–promoter interactions.
High-​resolution imaging of chromatin contacts can
be achieved using cryo-​FISH, in which standard FISH
probes are hybridized to thin (~100–200 nm) cryosections from cells fixed using conditions optimized
to preserve the nuclear ultrastructure; the signal is
then visualized using fluorescence or electron micro­
scopy10,19,25–27. More recently, the short length and high
specificity of fluorophore-​t agged oligonucleotides
known as Oligopaints28 have made it possible to target
15-kb loci using conventional microscopy29 or 5-kb
regions using super-​resolution microscopy (when
combined with a second labelling step to enhance the
fluo­rescence signal)30. Oligopaints are not derived from
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cloned genomic regions but are instead generated
from synthetic libraries of short (~60–100 bp) oligonucleotides, which are produced by massively parallel
synthesis31. Once generated, the library pool can be
amplified in a flexible manner, using different primer
pairs to give rise to different sets of FISH probes. The
ease of design of Oligopaints has opened new possibilities for the study of chromatin folding, such as being
able to visualize chromatin in different epigenetic states
at a resolution of tens of nanometres22. Oligopaint-​
based FISH has also been used in combination with
high-​throughput imaging to generate low-​resolution
contact maps (for example, at the TAD level) of whole
chromosomes7 and high-​resolution (30-kb) contact
maps for stretches of DNA 1.2–2.5 Mb in length32. In
addition, molecular beacon FISH probes have emerged
as a way to target genomic regions as short as 2.5 kb
(ref.33). In an unbound state, these probes form a hairpin
loop that minimizes the off-​target fluorescent signal by
bringing together the fluorescent label and a quencher.
By reducing the background signal from the unbound
probe, the technique improves the visualization of small
genomic regions.
Nuclear lamina
A protein mesh, consisting of
lamins and other membrane-​
associated proteins, at the
inner nuclear membrane that
contributes to nuclear
structure and function.
Chromatin in the proximity
of the lamina tends to be
heterochromatic and
transcriptionally repressed.
Fluorescence in situ
A technique that can be used
to visualize the location of
nucleic acid sequences within
the nucleus using sequence-​
specific fluorescent probes that
hybridize to the regions of
interest, combined with
Chromosome conformation
(3C). A technique used to
detect the frequency of
interactions between any
specified two loci in the
genome. Interactions between
loci are captured by
formaldehyde fixation, followed
by restriction enzyme digestion
and ligation. The frequencies of
interactions between loci are
determined by quantitative
real-​time PCR.
(High-​throughput chromosome
conformation capture).
A genome-​wide version of
chromosome conformation
capture that allows all chromatin
interactions in the genome to
be mapped simultaneously.
The frequencies of interactions
between loci are determined
by paired end sequencing.
Proximity ligation
Fixation of cells, followed by
fragmentation of chromatin
and ligation of nearby,
crosslinked DNA fragments.
Genome architecture
(GAM). A genome-​wide
approach to detect chromatin
contacts based on their
physical distances within the
nucleus. DNA loci are detected
in thin nuclear slices by DNA
extraction and sequencing.
Chromatin contacts are
inferred from co-​segregation
frequencies of pairs of DNA
loci across a large (400–1,000)
collection of nuclear slices.
Live-​cell imaging of nuclear structures. Chromosome
folding is a highly dynamic process that varies greatly
throughout the cell cycle 34,35. Our ability to study
these chromatin dynamics has been revolutionized
by technologies based on genome editing that allow
specific genomic loci to be targeted in live cells. Early
iterations of this approach were rather laborious; cell
lines needed to be created in which the target locus
was tagged with DNA binding site arrays that recruit
a fluorescently tagged cognate DNA binding protein
(such as the Lac operator-​repressor32,33, Tet operator-​
repressor34 and ANCHOR35 systems). Now, loci can be
targeted in live cells with a version of the CRISPR system that uses an endonuclease-​deficient form of Cas9
(dead-​Cas9 (dCas9)) fused with a fluorescent protein36.
The tagged dCas9 is recruited to the genomic locus of
interest via its interactions with sequence-​specific small
guide RNAs (Fig. 2). For simultaneous labelling of two
genomic regions, small guide RNAs can be differentially modified to act as scaffolds that bring fluorescent
proteins to the target loci. For example, fusion proteins
that comprise a fluorescent protein and either tandem
dimer MS2 coat-binding protein (tdMCP) or tandem dimer PP7 coat-​binding protein (tdPCP) can be
directed to target loci by guide RNAs containing MS2
or PP7 aptamers, respectively. As both proteins have
a comparably high exchange rate, which compensates
for photobleaching, this approach is also well suited to
long-​term live-​cell imaging37–39. However, most CRISPR-​
based methods are currently limited to the detection of
repetitive sequences because they rely on a single species
of guide RNA, which hybridizes to identical genomics
sequences, to direct simultaneous binding of dozens
of copies of the fluorescent protein to achieve a strong
fluorescent signal. A notable exception is the chimeric
array of gRNA oligonucleotides (CARGO); by delivering 12 different guide RNAs into a single cell, this
technique was able to efficiently label a non-​repetitive
2-kb genomic region40.
Ligation-​based detection of contacts
3C-​based methods extract chromatin interaction fre­
quencies between genomic loci via chromatin cross­
linking and proximity ligation (Fig. 3) . Following
formaldehyde fixation to capture protein-​mediated and
RNA-​mediated contacts, chromatin is fragmented using
a restriction enzyme, and the crosslinked restriction
fragments are ligated41. The purified ligation fragments
are called a 3C library. The ligation frequency between
two loci of interest can be quantified by PCR using
appropriate primer pairs. Thus, 3C focuses on interactions between two loci (‘one versus one’) and requires
prior knowledge of the targets of interest. However, the
3C library contains all ligation products for the genome
investigated and the 3C workflow can therefore be
adapted to enable genome-​wide analysis of chromatin
contacts. Chromosome conformation capture-​on-chip27
or circular chromosome conformation capture42, both
called 4C, enrich for interactions of one region with the
remaining genome (‘one versus all’). Chromosome conformation capture carbon copy (5C)43 captures contacts
of a larger genomic stretch at high resolution (‘many versus many’). Finally, Hi-​C44 captures all ligation events
across the entire genome (‘all versus all’). Workflows
and differences between these techniques have been
described elsewhere in great detail45. Here, we focus on
the most commonly used versions (Fig. 3; Table 1).
Mapping all contacts at a single locus with 4C. A straight­
forward and cost-​effective method to obtain additional
information from a 3C library is 4C. Here, primers for
a region of interest (such as a promoter) are used to
amplify all ligation partners of the locus under investigation (called the ‘viewpoint’) (Fig. 3). The amplified ligation products are sequenced (to a depth of 1–5 million
reads per library46) and used to analyse genome-​wide
interaction partners of the region of interest at a resolution of a few kilobases. 4C has been widely used to
investigate cis-​regulatory landscapes of genes, especially in development and disease47. It is well suited
for detecting short-​range regulatory interactions48, but
has also been applied to detect contacts spanning long
genomic distances, including whole chromosomes27,49.
Mapping all contacts occurring within a large genomic
region with 5C. In 5C, large genomic regions spanning up to several megabases are amplified from the
3C library using an elegant, yet complex, mix of forward and reverse primers. For example, 5C analysis of
a 4.5-Mb chromosomal region around the Xist gene
revealed the presence of TADs24. 5C has the advantage of producing high-​resolution data at an afford­
able sequencing depth (~60 million reads per library
to obtain resolution of 15–20 kb for a 1-Mb region)50.
However, the resolution of 5C is dependent on the ability
to design forward and reverse primers for all possible
restriction fragments across a given locus; in the absence
of appropriate primers, some mappable fragments will
be excluded from the contact map.
Mapping all contacts at one or more loci with capture-​
based methods. A 3C library can be enriched for one
or more genomic targets of interest using capture-​based
methods, such as Capture-​C51, Capture Hi-​C52 and
CAPTURE53. In these approaches, biotinylated oligonucleotides complementary to a genomic region of interest
are used to pull-​down specific ligation products from
the library, which are then amplified and sequenced.
These approaches can be used to detect interactions of
one viewpoint but also of entire genomic regions47 or
groups of targets54,55.
Mapping all genome-​wide contacts with Hi-​C and its
derivatives. Hi-​C is the most commonly used genome-​
wide approach to map chromatin contacts from a
3C chromatin preparation44. In this approach, the ends of
crosslinked DNA restriction fragments are labelled with
biotin and then ligated. After ligation, the exonuclease
activity of T4 DNA polymerase is used to remove the biotin label from the ends of unligated fragments. Ligated
DNA fragments
DNA fragment
Streptavidin bead
Biotin labelling
Removal of
biotin from
Gel analysis or
quantitative PCR
all vs all
many vs all
One vs one
One vs all
Many vs many
Fig. 3 | 3c and its derivatives. Chromosome conformation capture (3C)-based assays measure contact frequencies of
pairs of DNA loci by proximity ligation of crosslinked and fragmented chromatin. All 3C-​based assays involve fixation
of the chromatin, isolation of nuclei and DNA fragmentation (for example, with a restriction enzyme). The obtained
crosslinked chromatin fragments are then processed for 3C, circular chromosome conformation capture (4C) or
chromosome conformation capture carbon copy (5C), which map chromatin contacts for preselected regions, or for
genome-​wide assays, such as high-​throughput chromosome conformation capture (Hi-​C) and proximity ligation-​assisted
chromatin immunoprecipitation sequencing (PLAC-​seq). In 3C, 4C and 5C, the crosslinked chromatin fragments are
ligated and the DNA is purified. In 3C, the interactions between two chosen genomic regions are detected by PCR
amplification with primers specific to the two regions of interest. PCR products are analysed semi-​quantitatively on an
agarose gel or by real-​time quantitative PCR. Interactions are defined by higher ligation frequencies compared with
control regions of similar genomic distance. In 4C, interactions of one viewpoint with the whole genome are measured.
The ligated and purified DNA is fractionated with a secondary restriction digest, and the digested, smaller DNA fragments
are circularized and amplified with primers facing outwards from the viewpoint. The PCR products are sequenced by
paired end sequencing, providing the sequence information and frequency of every chromatin contact of the viewpoint.
In 5C, the ligated and purified DNA is directly amplified using primers for all restriction fragments within a consecutive
genomic region, usually hundreds of kilobases up to several megabases. The PCR products are sequenced and provide
information about the ligation frequencies of all fragments within the region of interest. In Hi-​C and PLAC-​seq, digested
DNA fragments are labelled with biotin, ligated and then fragmented further by sonication. In PLAC-​seq, DNA fragments
bound to a protein of interest are pulled-​down by immunoprecipitation. Then, in PLAC-​seq and Hi-​C, the DNA is purified,
biotinylated nucleotides are removed from unligated fragment ends and all ligated DNA fragments are pulled-​down with
streptavidin beads. After pull-​down, DNA fragments are sequenced and provide information about the interaction
frequencies of all pairs of loci in the genome (Hi-​C) or the interactions mediated by a protein of interest (PLAC-​seq).
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Split-​pool recognition of
interactions by tag
(SPRITE). A ligation-​free
approach to detect chromatin
interactions by tagging
crosslinked chromatin
complexes. The DNA (and
RNA) molecules within an
individual chromatin complex
are identified after sequencing
by their unique combination of
barcodes that have been
sequentially added using a
split-​pool strategy.
(ChIP). A method used to
determine whether a given
protein binds to, or is localized
to, specific chromatin loci
in vivo, detected after (native
or crosslinked) chromatin
purification and immuno­
precipitation, followed by DNA
detection by PCR, microarray
hybridization or sequencing.
fragments, which retain the biotin label, are enriched
using streptavidin beads to minimize the number of
unligated DNA molecules in the sequencing library.
Depending on the enrichment efficiency, about 50–70%
of sequencing reads map to pairs of ligated restriction
fragments in Hi-​C libraries56. In tethered chromosome
capture (TCC)57, an early modification of Hi-​C, the detection of unspecific ligation events between non-​crosslinked
material is minimized by tethering the crosslinked and
biotinylated chromatin to streptavidin beads before ligation. This approach detects more long-​range intrachromosomal contacts and contacts between chromosomes
than standard 3C-​technologies57. By contrast, genome
conformation capture (GCC)58, an approach developed
at the same time as Hi-​C, sequences all DNA present in
the 3C library, without preselection of ligated fragments.
Although currently much more expensive, especially for
large genomes, GCC has the advantage of allowing direct
normalization of DNA abundance, thereby controlling
for biases in sequencing and for the presence of genomic
alterations, such as copy number variations. Methods for
detection and normalization of copy number variations
have also recently been developed for Hi-​C59–61.
Many other variants of genome-​wide 3C-​methods
have been reported, ranging from technical optimizations of the original Hi-​C protocol (such as DNase
Hi-​C62,63 and in situ Hi-​C64) and advances to improve
resolution (such as Micro-​C)65–67, to protocols based on
the enrichment of contacts mediated by specific proteins
or open chromatin regions (open chromatin enrichment
and network Hi-​C (OCEAN-​C)68). Currently, the most
commonly used version is in situ Hi-​C. In the original
Hi-​C protocol, sodium dodecyl sulfate (SDS) is used to
disrupt the nuclear membrane and ligation of crosslinked
DNA therefore occurs partially in solution. In situ Hi-​C
omits this SDS step, allowing ligation of chromatin fragments within the presumably more native environment
of the intact nucleus. As a result, the number of random
ligation events is reduced and signal-​to-noise ratios are
improved, thereby reducing the sequencing depth and
enabling higher-​resolution contact maps. However,
detailed analyses of the nuclear fragments that contribute to contacts in the original version of Hi-​C showed
that large portions of the chromatin were thought to
remain inside the partially digested nucleus during ligation69. Nonetheless, the in situ Hi-​C protocol is faster and
easier than the original version64, mainly because it does
not require extensive dilution of the crosslinked chromatin prior to DNA ligation. Consequently, all subsequent
steps can be conducted in smaller volumes, allowing
more efficient ligation and DNA extraction. Easy Hi-​C is
another recent approach to simplify Hi-​C70. It avoids biotin enrichment and can be used with lower cell numbers
than standard Hi-C (Table 1).
Mapping genome-​wide contacts in single cells with singlecell Hi-​C. Standard Hi-​C generates average contact
maps from millions of cells, without any possibility to
understand heterogeneity of the cell population. Single-​
cell Hi-​C overcomes this limitation by allowing Hi-​C
contact maps to be produced from individual cells isolated during the process of generating Hi-​C libraries71,72.
This approach allows rare cell types to be studied73 and
helps chromosome structures to be determined at specific stages of the cell cycle74. The single-​cell Hi-​C protocol involves in situ proximity ligation of crosslinked and
digested chromatin, followed by isolation of single nuclei
from the cell suspension and generation of sequencing
libraries from each nucleus71,74. Single-​cell combinatorial indexed Hi-​C (sciHi-​C) adopts a different approach;
instead of isolating single cells, DNA within each nucleus
is tagged with a unique combination of barcodes75.
First, cells are fixed, lysed and digested with a restriction enzyme. Then, the cell suspension of digested, but
intact, nuclei is split into 96-well plates, indexed with
individual barcodes, pooled and split again. After several
rounds of indexing, in situ proximity ligation and library
preparation are performed on pooled nuclei, allowing
high-​throughput generation of single-​cell Hi-​C libraries.
One of the major challenges in single-​cell Hi-​C is the
efficient recovery of contacts: inefficient digestion and
ligation and incomplete recovery of input material result
in contact maps that represent only a proportion of the
contacts that may exist in a single cell. Modifications of
the original protocol increased the average number
of contacts detected in one cell from ten thousands up to
hundreds of thousands34,74, but this remained a fraction
(2–5%) of the possible contacts in the genome. Recently,
the development of Dip-​C (diploid chromatin conformation capture) has increased the number of detectable
contacts to an average of 1 million per cell by omitting
biotin incorporation and including a whole-​genome
amplification step in the protocol76.
Combining 3C-based approaches with chromatin
immuno­precipitation. 3C-based methods can be used to
study chromatin contacts mediated by specific proteins,
such as chromatin modifiers, architectural proteins, mem­
bers of the transcription machinery or cell type-specific
transcription factors. To explore contacts that coincide
with chromatin occupancy of specific proteins, Hi-C
libraries can be enriched by chromatin immunoprecipitation
(ChIP) before ligation. Early methods, such as ChIPloop77 and enhanced 4C-ChIP (e4C)78, required that
chromatin be solubilized to enable specific immuno­
precipitation before ligation. However, standard 3C conditions often do not fully solubilize chromatin, as nuclei
stay mostly intact after SDS treatment69, resulting in low
signal-​to-noise ratios. Other approaches, such as chromatin interaction analysis by paired-​end tag sequencing
(ChIA-​PET), included sonication of the nuclei, as is more
typically used for ChIP79. Although sonication allows
efficient precipitation of chromatin, its influence on the
outcome of the subsequent proximity ligation remains
unclear. Challenges in implementing ChIA-​PET have
led to other strategies for combining ChIP with Hi-C,
namely Hi-​ChIP80 and proximity ligation-​assisted chromatin immunoprecipitation sequencing (PLAC-seq)81.
Instead of performing protein pull-​down followed by
ligation of DNA fragments, Hi-​ChIP and PLAC-seq perform in situ Hi-​C and proximity ligation before sonication and immunoprecipitation. In this order, the ligation
occurs in intact nuclei under optimal conditions, before
chromatin contacts specific to the protein of interest are
enriched. Regardless of these increased efficiencies, the
results from immunoprecipitated 3C-libraries should
be interpreted carefully because of the bias introduced
by enriching for genomic regions that are bound by the
protein of interest82.
Ligation-​free detection of contacts
The reliance of 3C-​based approaches on the ligation
of the ends of DNA fragments found in a cluster of contacts favours the detection of ‘simple’ chromatin contacts
which involve two or a few genomic regions. This bias
occurs because each DNA fragment can ligate with only
one or two other fragments, so not all instances of every
interaction in a complex cluster are detected84. Thus,
the full interactome of each DNA fragment is diluted
by the choice of only one or two other fragments during
ligation. Recently, three ligation-​free approaches have
been developed for genome-​wide mapping of chromatin contacts: GAM10, SPRITE11 and ChIA-​Drop12. These
methods are orthogonal to ligation-​based approaches
and are starting to provide new insights into 3D genome
topology. Other ligation-​free approaches — tyramide
signal amplification (TSA-​s eq)85 and DNA adenine
methyltransferase identification (DamID)86–88 — map
chromatin with respect to nuclear landmarks (such as
the nuclear lamina or various nuclear bodies), thereby
helping to define chromatin positions in 3D space.
ensure only methylated binding sites are amplified and
sequenced. In an interesting adaptation called targeted
DamID (TaDa)88, expression of the Dam fusion protein
is restricted to a specific cell type of interest, using tar­
geted expression systems (such as the Gal4–UAS
system), which allows detection of DNA–protein interactions, in a cell type-​specific manner without prior isolation or sorting of cells. DamID has been successfully
used to study DNA interactions with proteins such as
Lamin B1, which resulted in the genome-​wide mapping
of lamina-​associated domains and provided spatial
information about chromatin with regard to the nuclear
periphery89,90. However, interactions between chromatin
and other nuclear compartments, such as splicing speckles, are not readily detected with DamID because most
of the DNA surrounding these compartments does not
directly bind to the tagged proteins91.
TSA-​seq addresses this problem using tyramide signal amplification to measure the distances between chromatin and nuclear compartments92. In this approach,
horseradish peroxidase (HRP) is conjugated to an antibody that binds to a protein specific to the nuclear compartment of interest, where it catalyses the production of
biotin-​conjugated tyramide free radicals, which diffuse
and bind to nearby macromolecules — including DNA.
Biotin-​labelled DNA can be subsequently selected by
biotin pull-​down and sequenced to identify all genomic
regions that were close enough to the protein of interest
to be labelled. TSA-​seq has been used to map genome-​
wide the distances between all genes and their nearest
splicing speckle92.
Another recent adaptation of DamID, called DamC,
detects 4C-​like contacts between a target region and
the surrounding DNA regions, up to distances of a few
hundred kilobases93. In DamC, Dam is fused with the
reverse tetracycline receptor (rTetR), which binds to Tet
operator sites inserted at the genomic region of interest.
The Dam fusion protein methylates the target and its
interaction partners in vivo. When combined with high-​
throughput sequencing, DamC reveals chromatin contacts independently of crosslinking or ligation, but unlike
the other 3C-​methods and ligation-​free approaches it
requires engineering of the cells of interest. Comparison
of DamC data with 4C and Hi-​C data showed high similarities at the level of TADs and CTCF loops at many
genomic sites; however, some differences at loops and
sub-​TAD structures could also be observed93.
Mapping contacts with nuclear structures with DamID
and TSA-​seq. DamID is an in vivo genome-​wide method
for detecting interaction sites between a protein of interest and DNA. The DNA binding domain of the protein
of interest (for example, RNA polymerase II (Pol II))
is fused to the DNA adenine methyltransferase (Dam)
protein from Escherichia coli86–88, which specifically
methylates adenines in the sequence GATC. When
the fusion protein is expressed at low levels in cells,
GATC sequences within or close to DNA binding sites
of the protein of interest are marked by methyl­ation.
After DNA extraction, the methylated GATC sites are
cut with a methylation-​sensitive restriction enzyme
and adapters are added to the restriction fragments to
Mapping all genome-​wide contacts with GAM. In GAM,
nuclei are sectioned in random orientations from a
popu­lation of fixed and sucrose-​embedded cells using
ultra-​thin cryosectioning (220 nm thickness). Single
nuclear slices are then isolated directly from the cryosection by laser microdissection. GAM thus avoids
cell extraction or sorting, both of which can disrupt
cellular and nuclear structures, which can be especially
important when analysing complex tissues. The DNA
from every slice is extracted, whole-​genome amplification is performed and indexed sequencing adapters
are added before the DNA from all slices is pooled for
sequencing (Fig. 4). From the sequencing data for several hundred nuclear sections, each from a single cell,
Genomic resolution of genome-​wide 3C-​methods. A
major consideration for any genome-​wide technique is
genomic resolution. Hi-​C data represent interaction frequencies between genomic regions in a contact matrix,
consisting of equally sized genomic bins. The bin size
(resolution) depends almost entirely on the sequencing
depth. Resolutions of 30 kb or lower are often preferred
to study the chromatin domain and compartments but
also long-​range contacts between large genomic regions
(such as TADs); using standard Hi-​C, this requires
sequencing depths of approximately 200–400 million
reads in mammalian genomes. However, billions of reads
become necessary for high-​resolution (1-kb) data sets of
the human genome that can provide detailed insights
into 3D genome topology64. Recently, a computational
approach, HiCPlus, applied deep learning to infer high-​
resolution contact matrices from low-​resolution Hi-​C
data, which reduced the sequencing depth required to
obtain a given resolution by a factor of 16 (ref.83).
Genomic resolution
The size of the window (often in
the range of kilobases) when,
for most assays, reads after
sequencing are mapped to the
genome and then binned into
equally sized genomic windows
Sequencing depth
The average number of reads
representing a given nucleotide
in the reconstructed sequence.
A 10× sequence depth means
that each nucleotide of the
transcript was sequenced,
on average, 10 times.
Nuclear bodies
Membrane-​less compartments
in the nucleus with high
concentrations of DNA binding
proteins, chromatin modifiers
or RNAs that can be involved in
shaping chromatin structure
and modulating gene
regulation. Nuclear bodies
include the nucleolus, splicing
speckles and Polycomb bodies.
Nature Reviews | Genetics
Chemical fixation
Chemical fixation, nuclear
isolation, sonication, DNase digest
Chemical fixation, nuclear
isolation, sonication, DNase digest
Laser microdissection
Droplet with
Fragmented chromatin
Microfluidics device
(repeat ×5)
Barcoded complexes
are redistributed
Whole-genome amplification
Identification of clusters
Identification of clusters
Genomic region
Nuclear slice
1 2 3 …
B +
C +
Co-segregation frequencies
Interaction frequencies
based on cluster co-occurrence
chromatin contacts between pairs of DNA loci can be
inferred by counting their co-​segregation frequency
(that is, how often the two loci are contained in the
same nuclear sections). Genomic regions that are closer
in 3D space are more frequently found in the same
nuclear slice. To detect statistically significant inter­
actions, GAM was combined with a mathematical
Interaction frequencies
based on cluster co-occurrence
model, statistical inference of co-​segregation (SLICE)10.
The most specific chromatin contacts detected with
SLICE were found to contain active genomic regions,
such as active enhancers and actively transcribed genes,
with these contacts extending over megabases up to
entire chromosomes10. SLICE separately models the random interactions that depend on genomic distance and
◀ Fig. 4 | Ligation-​free methods to map chromatin contacts genome-​wide. a | Genome
architecture mapping (GAM) measures co-​segregation frequencies of genomic regions
by slicing the nucleus into thin nuclear sections and sequencing the DNA content of a
large number of randomly collected slices. To obtain nuclear slices, cells are fixed and
cryosectioned. Single nuclear slices are isolated from the cryosection using laser
microdissection. DNA is extracted from each nuclear slice by whole-​genome amplification
and sequenced. The sequence information is used to score the presence or absence of
genomic loci in each slice. Spatial proximity of all pairs of loci in the genome is inferred
from the frequency of their co-​occurrence in the population of slices. b | Split-​pool
recognition of interactions by tag extension (SPRITE) detects chromatin interactions
of multiple genomic regions by tagging single crosslinked chromatin complexes with
unique combinations of identifiers before sequencing. Cells are fixed and the crosslinked
chromatin is fragmented using sonication. The resulting chromatin complexes are
split into wells of a 96-well plate, and DNA in each well is ligated to a unique barcoded
adapter. The contents from all wells are pooled and split again, followed by adapter
ligation. The process is repeated five times so that each chromatin complex is labelled
with a unique combination of adapter sequences. DNA is purified and sequenced, and
the adapter combination of each sequenced DNA fragment is used to identify all
genomic regions that share the same combination of adapters and were, therefore,
initially crosslinked together, inferring spatial proximity. c | Chromatin-​interaction analysis
via droplet-​based and barcode-​linked sequencing (ChIA-​Drop) detects chromatin contacts
by barcoding crosslinked chromatin complexes after cell fixation, lysis and chromatin
fragmentation. Barcodes are delivered in a droplet that contains a unique identifier and
reactions for adapter ligation and DNA amplification. Each chromatin complex is loaded
onto a droplet in a microfluidics device and sequenced. Barcodes identify regions from
the same droplet, indicating regions that were crosslinked due to spatial proximity.
the specific interactions that occur at a given physical
distance (for example, below 100 nm)10; it interrogates
which pairs of loci co-​segregate more often in the collection of slices than expected from random contacts,
and quantifies the frequency of specific interaction in
the cell population. GAM also allows genome-​wide
interactions between three or more DNA loci to be
detected simultaneously, and has detected long-​range
contacts between TADs containing super-​enhancers and
highly-​transcribed TADs10. The resolution of GAM data
sets depends on the number of nuclear slices collected.
With 400 nuclear slices, sequenced with ~1 million
reads per slice, it was possible to achieve a resolution
of 30 kb for pairwise chromatin contacts10, comparable
with a Hi-​C library with similar sequencing depth94.
Larger GAM data sets comprising a few thousand
nuclear slices will help define the maximal resolution
that can be practically afforded by GAM.
Mapping all genome-​wide contacts with SPRITE and
ChIA-​Drop. SPRITE11 and ChIA-​Drop12 detect chromatin interactions by tagging crosslinked chromatin complexes. Similar to 3C-​based approaches, these methods
rely on mild fixation and fragmentation of chromatin
inside the nucleus – but unlike 3C-​based approaches,
they do not use proximity ligation. Instead, in SPRITE,
the crosslinked chromatin fragments are split across a
96-well plate, where each well contains a unique barcode
(Fig. 4). The indexed chromatin complexes are re-​pooled,
followed by sequential rounds of splitting, barcoding
and pooling. The DNA (and RNA) molecules within
an individual chromatin complex are identified after
sequencing by their unique combination of barcodes
added using this split-​pool strategy; only DNA fragments that were crosslinked with each other will display
the same combinations of barcodes. In ChIA-​Drop,
crosslinked and fragmented chromatin is separated into
Nature Reviews | Genetics
single chromatin complexes by droplet formation using
a microfluidics device. Each droplet contains reagents for
barcoding and amplification, and barcoded complexes
are pooled and sequenced, as in SPRITE. SPRITE detects
TADs and loop domains, both of which are features of
Hi-​C contact maps. However, SPRITE also detects additional genome-​wide features of nuclear architecture, such
as the association of specific genomic regions with nucleoli and splicing speckles. The predominant chromatin
hubs around these nuclear bodies contain genomic
regions from different chromosomes, an observation
that is in agreement with single-​cell imaging95 but that
had not been made using 3C-​based assays. SPRITE also
detects long-​range contacts between regions containing
active genes and super-​enhancer regions that were first
recognized as being multiway-specific interactions in a
study using GAM10.
Comparing approaches
Fundamental differences exist between current appro­
aches for mapping 3D genome folding, including how
the chromatin is fixed and prepared, their power to
detect multiple chromatin contacts or contacts with different spatial distances and protein occupancy, and their
ability to detect long-​range contacts within the same
(Fig. 5) or different chromosomes. These diffe­rences have
sometimes led to observations that can be difficult to
reconcile between the different approaches.
Fixation and chromatin preparation. With the exception
of live-​cell methods (such as DAM-​based and CRISPR-​
based approaches), all chromatin folding techniques
start by crosslinking DNA–protein complexes to stabilize nuclear structures (Table 2). Chemical fixation
using formaldehyde is the most common approach
for crosslinking, but concentrations, buffers and fixation times vary widely; for example, 1% formaldehyde
is typically used for 3C-​based methods, 4% for most
DNA-​FISH experiments in whole cells and 8% for GAM
or cryo-​FISH in nuclear slices. Other fixatives include
solvent-​based precipitation using ethanol, methanol or
acetone. A recent imaging study compared the effects
of formaldehyde fixation and cryofixation on nuclear
structure using partial wave spectroscopy96. It revealed
that weaker fixatives (such as 4% formaldehyde in PBS)
introduce larger structural distortions than stronger fixatives (such as glutaraldehyde, often used for electron
microscopy). However, the distinction between condensed and decondensed chromatin remains detectable
at the population level96, which is consistent with the
ability of all current chromatin folding methods to successfully map euchromatin and heterochromatin. The
effect of varying crosslinking conditions (from no fixation to 5% formaldehyde fixation) has been examined
in Capture-C experiments; similar short-range inter­
actions were detected under all conditions, but formal­
dehyde concentrations below 2% improved the efficiency
of detection97. Our own previous work showed that the
organization of the active form of Pol II, which marks
transcription sites, can be highly disrupted with weaker
fixatives, but not with the fixation regimen used for
GAM or cryo-​FISH98. In FISH, denaturation of the DNA
Probability of interaction
0.05 0.15 0.25
sequencing depth:
500 million reads
FISH probes
(500 kb)
30 Mb
65 Mb
65 Mb
10-Mb distance –
9% contacting
sequencing depth:
240 million reads
18-Mb distance – 74% contacting
30 Mb
28-Mb distance – 18% contacting
2 μm
2 μm
Normalized contact frequency
Interacting loci
30 Mb
Non-interacting loci
SPRITE (clusters 2–10)
SPRITE (all clusters)
In situ Hi-C
sequencing depth:
800 million reads
65 Mb
Genomic distance (bp)
Fig. 5 | comparison of long-​range chromatin contacts across methods.
a | Genome architecture mapping (GAM) detects significant interactions
between super-​enhancers (circles 1 and 2) that span large genomic distances
(18 Mb and 28 Mb). The heat map shows GAM interaction probabilities for
chromosome 11 (region 30–65 Mb) in mouse embryonic stem cells (ESCs) at
500-kb resolution10. Contacts between the super-​enhancer regions can also
be detected using cryo-​fluorescence in situ hybridization (cryo-​FISH), which
confirmed their interactions in a high percentage of cells in the population:
the 18-Mb distant super-​enhancer contact and the 28-Mb distant contact
were detected in 74% and 18% of cells, respectively. By comparison, contacts
between a super-​enhancer and a 10-Mb distant control region (circle 3) not
detected by GAM were detected in only 9% of cells. The images show DAPI-​
stained cryosections with interacting and non-​interacting 500-kb FISH probes.
b | Long-​range super-​enhancer contacts can also be found when looking at
GAM contacts without filtering for the most significant interactions
(~500 million reads, 40-kb resolution, mouse ESCs, data from ref.10), and
although they are not readily detected in Hi-​C data with an average
sequencing depth (~240 million reads, 40-kb resolution, mouse ESCs, data from
ref.94), they start to emerge in deep-​sequenced in situ Hi-​C data (~800 million
reads, 50-kb resolution, mouse ESCs, data from ref.191). Heat maps were
generated by Christophe Thieme from the published, normalized matrix files.
30 Mb
65 Mb
Scores are colour-​coded, where the colour-​code range (maximum and
minimum cutoffs) is determined by the mean value of the bin distances 1–20
and –50 to –30 from the diagonal, respectively. c | The plot shows the
distribution of contact frequencies detected by high-​throughput chromosome
conformation capture (Hi-​C), GAM and split-​pool recognition of interactions
by tag extension (SPRITE) (all clusters or clusters with 2–10 reads) along
the linear genomic distance of chromosome 11 in mouse ESCs, scaled to the
maximum observed value in each data set. The ligation-​free methods GAM and
SPRITE detect similar ranges of chromatin contacts, which can extend over
large genomic distances. By contrast, Hi-​C contacts typically extend
over shorter genomic distances. However, SPRITE data can be sorted based on
the number of interactions within one chromatin complex. When considering
only small SPRITE clusters with fewer than 10 genomic regions in the same
chromatin cluster, the range of detection between Hi-​C and SPRITE is
comparable, indicating that Hi-​C favours less complex short-​range contacts
over long-​range interactions involved in chromatin hubs with many interaction
partners. The plot was generated using the same data for GAM (ref.10) and Hi-​C
(ref.94) as used in part b, and data provided by Sofia Quinodoz for normalized
SPRITE clusters for chromosome 11, according to figure 3B of ref.11. Part a
adapted from ref.10, Springer Nature Limited. Heat maps in part b and plot in
part c courtesy of Christoph Thieme, Max Delbrueck Center, Germany.
Table 2 | experimental differences between chromatin contact assays and their effects on nuclear structures
chromatin preparation
effects on chromatin
Hypotonic treatment,
followed by
methanol–acetic acid
for 10 min
Permeabilization, HCl
treatment, heat and
formamide denaturation,
physical flattening of cells
through gravity and drying
The fixation process flattens cells, alters the
shape of the nucleus and the structure of
chromocentres, and changes the association of
some loci with their chromosome territories206–209;
denaturation can lead to loss of DNA210
2–4% depolymerized
PFA for 10 min
(fixatives are buffered
with PBS)
Permeabilization, HCl
treatment, heat and
formamide denaturation
Fixation can cause nuclei to increase in size,
accompanied by a change in the positions of
distant chromatin domains; overall, nuclear
structures and the distribution of chromatin
in the nucleus remain intact; denaturation
disrupts the nuclear membrane, accompanied
by loss of some heterochromatic regions and
redistribution of histone H2B99
and GAM
4% depolymerized
PFA for 10 min,
followed by 8% PFA
for 2 h (fixatives are
buffered with 0.25 M
for cryoprotection,
cryosectioning. For
cryo-​FISH, slices are
permeabilized, treated
with HCl, heat and
formamide denaturation
Stringent, electron microscopy-​grade
fixation preserves nuclear and cytoplasmic
ultrastructures and is indistinguishable from
glutaraldehyde fixation98; robust fixation renders
nuclear structures more resilient to the negative
effects of heat denaturation for FISH, with good
preservation of nuclear compartments before
and after FISH25,101
1% formaldehyde
for 10 min (fixatives
are buffered with
cell culture medium
or PBS)
Cell lysis, SDS treatment,
fragmentation of the
genome (for example,
restriction digest, DNase
treatment, sonication)
Mild fixation is required to allow subsequent
restriction digest, but can result in redistribution
of nuclear proteins (similar fixation studied in
ref.98); after chromatin preparation, restriction
enzymes disrupt nuclear structures, nuclei
swell and chromatin is distributed more
uniformly69; DNA-​FISH on digested nuclei shows
that chromosome territories seem larger and
less condensed compared with fixed cells before
digestion, but DNA is maintained in territories69
3C, chromosome conformation capture; ChIA-​Drop, chromatin-​interaction analysis via droplet-​based and barcode-​linked
sequencing; DNA-​FISH, fluorescence in situ hybridization of DNA ; FISH, fluorescence in situ hybridization; GAM, genome
architecture mapping; HCl, hydrogen chloride; PFA , paraformaldehyde; SDS, sodium dodecyl sulfate; SPRITE, split-​pool
recognition of interactions by tag extension.
by heat and formamide induces fine structural changes
in chromatin folding, such as slight distortions of the
interchromatin space99,100. However, 3D-​FISH preserves
the organization of centromeres seen by imaging the
same cells before and after hybridization100, and cryo-​
FISH retains the organization of active Pol II sites101.
In another method, resolution after single-​strand exonuclease resection (RASER)-FISH, heat denaturation of
the DNA is avoided and DNA accessibility is achieved
by exonuclease digestion, thereby reducing the effects of
DNA denaturation102.
Multiplicity of chromatin contacts. The dependency
of 3C-​based methods on DNA end ligation results in
preferential detection of low-​multiplicity contacts that
involve only a few genomic regions84. However, every
3C library also includes interaction events that occur at
complex clusters involving more than two DNA fragments, albeit at a low representation. Current methods to
capture these higher-​complexity ligation events include
multi-​contact 4C (MC-4C)103, which uses long-read
sequencing (such as nanopore sequencing) of 4C libraries to capture three-​way contacts of a region of interest,
and chromosomal walks (C-​walks)104, which implement
multiple ligation steps followed by dilution and barcoding of the isolated ligation products. Alternatively,
methods such as the concatemer ligation assay (COLA)105
and Tri-​C106 generate 3C libraries with a restriction enzyme
Nature Reviews | Genetics
that cuts small DNA fragments, which increases the
frequency of detecting multiple ligation events in one
sequencing read. Estimates based on direct comparison
of pairwise and multiway ligation events indicate that
only 17% of chromatin contacts in mouse embryonic
stem cells (ESCs) are pairwise contacts and, therefore,
the majority of the genome is involved in higher-​order
contacts between more than two genomic loci104. These
observations are supported by a recent study using
SPRITE, which showed that classical ligation-​dependent
methods under-​represent higher complexity contacts11
(Fig. 5c). Assays that do not depend on ligation detect
DNA fragments that are in spatial proximity regardless
of the number of interacting genomic loci. For example, long-​range multiway contacts between genomic
regions harbouring super-​enhancers were readily found
in GAM10, FISH10 and SPRITE11 data, but had not previously been detected with Hi-​C. Furthermore, analyses of
triplet interactions between TADs in GAM showed that
multiple interactions between super-​enhancer regions
and active genes are a common feature of genome
conformation in mouse ESCs10.
Spatial distance between contacting genomic regions.
The spatial distance between genomic loci is thought to
influence the probability of ligation irrespective of the
frequency of contacts. Whereas cryo-​FISH and SPRITE
have readily detected abundant interchromosomal
contacts in human, mouse and Drosophila cells25,76,107,108,
3C-​based methodologies are more often used to explore
specific contacts within chromosomes, with some exceptions27,76,109–111. Recent CRISPR–Cas9 live-​cell imaging
of a small number of chromatin contacts within and
between chromosomes showed that interchromosomal contacts display spatial distances in the range
of ~280 nm, in contrast to distances of ~190 nm for
intrachromosomal interactions20. Interestingly, only
the intrachromosomal contacts could be observed in
matching Hi-​C data, indicating a dependency of close
spatial distances for successful proximity ligation.
Protein-​mediated interactions versus bystander contacts. GAM and all imaging-​based techniques collect all
possible spatial relationships between genomic regions,
regardless of their involvement in a protein-​mediated
interaction, and allow sampling of the whole range of
spatial distances within the interphase nucleus. Thus,
these methods also detect bystander contacts. However,
it is possible to identify the most specific contacts
through effective sampling to take into account all
behaviours of all genomic regions at all linear distances
across the cell population. In this regard, GAM currently
has more statistical power than FISH as it samples all
possible combinations, whereas FISH remains limited
to the analyses of a subset of regions or chromosomes.
Levels of concordance between different methods. The
validation of results obtained by 3C-​based methods
often entails the use of DNA-​FISH on a few selected
loci. Many examples show agreement between 3C interaction frequencies and spatial distances measured by
FISH, especially at large genomic distances44,64,112–115.
Loci in the same TAD are often closer in nuclear distance than loci in different TADs24,94, and interaction
frequencies obtained from Hi-​C correlate with spatial
distances at and above the TAD level115. A linear relationship between Hi-​C contacts and FISH distances was
found by investigating the physical distances between
all TADs along a chromosome115. An overall correlation
between Hi-​C interactions and the median spatial distance measured by high-​throughput FISH have recently
been shown for 90 pairs of loci. However, the range of
physical distances between genomic regions containing
Hi-​C interactors (with high ligation frequency) and non-​
interactors (with low ligation frequency) overlap extensively, with about 20% of distances being closer between
two non-​interactors than two interactors21. Thus, Hi-​C
captures spatial proximity but Hi-​C interactions are not
easily translated into physical distances. Other comparisons between FISH and 3C-​based methods have
also found non-​trivial relationships between physical
distance distributions and population-​average inter­
action frequencies113 and show that contact frequency is
distinct from average spatial distance, both in polymer
simulations and in experimental data116.
The use of FISH to validate Hi-​C results has helped
investigate false positives in Hi-​C data, assuming FISH is
correct, but is not a valid strategy for an unbiased search
for contacts that are missed by Hi-​C (that is, false negatives). Thus, any under-​represented contacts in Hi-​C
data have so far not been systematically studied. The
development of orthogonal genome-​wide ligation-​free
approaches, such as GAM and SPRITE, have been able
to identify new aspects of 3D genome folding that had
not been detected by Hi-​C but which are fully validated
by FISH10,11. The first and relatively small GAM data
set, combined with the mathematical model SLICE,
identified specific long-​range contacts across genomic
distances that span tens of megabases, which involve
active and enhancer-​rich genomic regions (Fig. 5a,b). One
promising outcome of the emergence of these orthogonal approaches is the development of analysis tools that
use the information they generate about such long-​range
contacts to discover the same contacts in Hi-​C data. In
this regard, it is interesting to note that CTCF depletion
in human cells results in the detection by Hi-​C of long-​
range contacts between super-​enhancers117, which raises
the possibility that CTCF-​mediated contacts may be
preferentially detected by Hi-​C in normal conditions, but
once CTCF-​dependent interactions are lost, other underlying folding patterns, including long-​range contacts,
become easier to detect.
The first SPRITE data set has also highlighted novel
aspects of 3D folding that are not readily captured by
Hi-C11. By discriminating contacts according to their
multiplicity, SPRITE shows a contact decay with genomic
distance that is Chromosome territories and interchromosomal very similar to Hi-​C when considering only
low-​complexity SPRITE clusters (2–10 genomic regions
per contact hub; Fig. 5c). By contrast, SPRITE shows a
striking abundance of long-​range contacts when considering also higher-​order contacts, which confirms early
theoretical predictions that ligation-​based approaches
are biased to the detection of more simple 3D chromatin
contacts84. Although GAM and SPRITE are orthogonal
methodologies, their frequency of contacts relative to
genomic distance are remarkably concordant10,11 (Fig. 5c).
Limitations and applications of different methodologies. Methods that use proximity ligation are limited by
the low efficiency of ligation, and are also potentially
affected by the local distance between, or the topology
of, the two DNA ends within the cluster of contacting DNA fragments. SPRITE also depends on ligation
of a small oligo to each DNA end in a contact cluster;
however, it is no longer dependent on the physical distance between two DNA fragments in the cluster, which
allows mapping of all contacts within one chromatin
complex. In 3C-​based methods and SPRITE, detection
of contacts depends on the efficiency of the fragmentation step to expose the DNA end. In GAM, there is
no DNA restriction digest or ligation, and the detection
of DNA depends on its extractability and sequencing
depth. 3C-​based methods, GAM, SPRITE and FISH
can be applied directly to cells, tissues or organisms,
whereas insertions of DNA binding site arrays (such as
the Lac operator system), CRISPR-​based imaging and
Dam-​related methods require genetic engineering of cell
lines or whole organisms, and will not be suitable for the
analyses of most human biopsies.
Each of the assays discussed here has different limi­
tations and applications, and thus contributes to our
current understanding of 3D genome folding in different ways. 3C-​based techniques have the advantage
of providing enormous amounts of chromatin contact
information in one comparably simple biochemical
experiment, although they may require high-​depth
sequencing when aiming for high resolution. 3C-​based
methods, and in particular proximity ligation itself, also
have important limitations that favour the detection of
more simple contacts over higher-​order chromatin contacts, which can lead to misunderstanding the importance and abundance of certain interactions. However,
3C-​based techniques are well suited for studying local
chromatin folding within the range of kilobases up to a
few megabases.
Imaging and ligation-​free methods have the ability to
detect chromatin contacts at all scales of chromosome
folding, including contacts between chromosomes.
GAM and SPRITE can be readily used for sequence-​
unbiased genome-​wide explorations, whereas detection
of contacts with DNA-​FISH remains limited to preselected loci and is most often used to validate findings
from genome-​wide techniques. Imaging fluorescently
labelled chromatin loci in live cells with CRISPR-​based
techniques will improve our understanding of possible
artefacts resulting from chromatin preparation or fixation. Other developments based on cryo-​focused ion
beam (cryo-​FIB) milling of intact, frozen cells118 or cryolysis119 also hold the potential of devising fixation-​free
versions of GAM and SPRITE that sample fractionated
frozen nuclei.
Insights into chromatin organization
Each of the techniques available for studying 3D chromatin folding has provided important structural and
functional insight into the different hierarchical levels
of chromatin organization.
Chromosome territories and interchromosomal contacts.
FISH imaging shows that specific chromosomes occupy
discrete non-​random nuclear spaces during interphase,
termed chromosome territories120 (Fig. 1). Chromosome
territories show cell type-​dependent preferences in
terms of both their radial position within the nucleus
and their position relative to other chromosomes25,107,121.
Specific contacts can be detected at the interface between
chromo­some territories20,122,123; overall, an estimated
20% of the volume of chromosome territories intermingles with other chromosome territories, often at
their peripheries, both in human primary lymphocytes24 and in Drosophila melanogaster cells25,108. The
extent of intermingling between chromosome territories directly correlates with translocation probabilities
upon ionizing radiation damage, highlighting that the
physical proximity between chromosomes affects their
stability in response to DNA damage25,124,125. The organization of chromosomes into discrete territories is also
inferred from 3C-​based and ligation-​free approaches,
as higher interaction frequencies are detected within
chromosomes than between them (Fig. 1). 3C-based
technologies have also detected contacts between chromo­
somes, and these have been successfully validated by
Nature Reviews | Genetics
Chromatin hubs and compartments. The organization of
chromosomes into subchromosomal domains has been
extensively studied. For example, in mammalian cells,
chromatin domains were observed in relation to replication origins, which contain many replicons and maintain their domain co-​association across subsequent cell
cycles129. The compartmentalization of chromosomes
into early and late replicating domains130–132 was also
shown to be linked to transcriptional activity, with sites
of active transcription occurring predominantly in early
replicating domains133. More recently, these observations
have been largely confirmed by genome-​wide assays to
map replication and transcription, in which transcriptionally active and early replicating chromatin domains
organize into separate subcompartments, distinct from
late replicating domains134–136. Early analyses of nuclear
organization by electron and confocal microscopy had
shown that chromatin occurs in highly condensed
(heterochromatic) and less condensed (euchromatic)
states 137, and revealed that transcription occurs in
euchromatic areas of the nucleus138. With the emergence of whole-​genome 3C-​based methodologies, such
as Hi-​C, the mapping of active and repressed chromatin
states has become possible at the genome-​wide scale,
providing powerful insights into how gene expression
relates to chromatin compaction. Application of principal component analysis to Hi-​C data revealed a strong
segregation of ligation events into two distinct compartments (A and B compartments) according to the activity
state of the genomic regions44. These compartments can
also be seen in contact maps generated by ligation-​free
approaches10,11 (Fig. 1). Comparisons with linear maps of
protein occupancy on chromatin helped reveal a strong
relationship between the A compartment and transcriptionally active, open chromatin, as defined by DNase
hypersensitivity, and the B compartment with closed
chromatin, defined by repressive epigenetic marks of
heterochromatin44. Increased depth of Hi-​C data sets has
since allowed smaller subcompartments to be detected,
which capture fine differences in replication timing as
well as preferred associations with the nucleolus or the
nuclear lamina64.
Nuclear compartments or domains. Nuclear compartments are membrane-​free organelles enriched for
specific nuclear proteins and RNAs, which often have
preferred associations with specific genomic regions and
thereby influence the large-scale organization of chromo­
somes during interphase. They include the nucleolus,
nuclear lamina, splicing speckles, paraspeckles, Cajal
bodies, promyelocytic leukaemia bodies, Polycomb
bodies, replication factories and transcription factories,
which have all been described initially using microscopy139,140 (Fig. 1). For example, active ribosomal gene
clusters are localized in the nucleolus, where the large
ribosomal RNAs are transcribed, processed and assembled into pre-​ribosomes141. Splicing speckles occupy
internal nuclear positions, separate from the nuclear
lamina and nucleoli, and bring together gene-​dense
regions91,142,143. Association between specific genes at
splicing speckles has been shown using imaging techniques, and has been confirmed at the genome-​wide
level with SPRITE, which revealed that regions from
different chromosomes come together at the same
speckles11. Genome-​wide mapping of gene association
with speckles has also recently been achieved by TSA-​
seq92. Fluorescence microscopy and electron microscopy
showed that transcription itself occurs at discrete sites
in the nucleus, termed transcription factories, which
may organize active transcription units144–146, with only
a small proportion of transcriptional activity (~5–10%)
being found immediately adjacent to the most prominent splicing speckles146. Interestingly, the fraction of the
genome that associates closely with slicing speckles has
been shown by TSA-​seq to contain highly transcribed
genes and super-​enhancers92, in keeping with previous
imaging data142. Co-​expressed genes can share the same
transcription factory, which may be compatible with
mechanisms of coordinated gene regulation via chromatin folding26,78,147,148, but it remains unclear whether
transcription factories are strictly specialized. Recent
findings show that several factors involved in the transcription process, such as Pol II149 or transcriptional
co-​activators BRD4 and MED1 (ref.150), can form condensates by liquid–liquid phase separation, a process that
may concentrate transcription factors and generate transcription factories. Moreover, the formation of nuclear
condensates has been suggested as a general principle of
nuclear body formation151. Clustering of distant genomic
regions is not only mediated by transcription, but also
occurs in the context of gene repression. Chromatin contacts at Polycomb bodies, which are repressive nuclear
compartments, are a prominent example of gene clustering. In D. melanogaster, Polycomb-​repressed Hox genes
come together over a genomic distance of 10 Mb when
they interact with a Polycomb body152. Other studies
have reported long-​range intrachromosomal and interchromosomal contacts between Polycomb-​bound genes
in human teratocarcinoma cells153 and in mouse ESCs52.
Understanding how the preferential associations of
genomic regions with specific nuclear domains relate to
3C-​derived chromatin contacts remains a major challenge. Comparisons of genome-​wide maps of lamina-​
associated domains90 and Hi-​C contacts show a strong
coincidence between the transcriptionally inactive B
compartment and the nuclear lamina64,94,154 or late replication domains136. Repressive histone marks that define
the heterochromatic B compartment are also strongly
enriched at genomic regions that associate with the
nucleolus155, suggesting that the compacted B compartment is both situated at the nuclear periphery and
clustered around the more central nucleoli, separated by
the active, open A compartment. However, the bimodal
separation of the A and B compartments derived from
3C-​technologies should not be naively inferred as strictly
active or silent chromatin. Genes can be activated in all
areas of the nucleus, including at the nuclear lamina156
or at centromeric regions157, and gene positioning at the
periphery does not always lead to gene inactivation158,159.
Heterochromatin domains also contain active sites of
transcription160. Consequently, a strict separation of the
A and B compartments, as defined by 3C-​approaches,
seems unlikely, especially considering that contacts
between compartments can be found in Hi-​C maps94 and
that they are found even more robustly using orthogonal methods, such as GAM, that do not rely on weak
fixations10. These observations suggest that long-​range
gene-​regulation mechanisms are complex, and not only
depend on pairwise contacts between genomic regions
but may also be influenced by the local nuclear environment where each region is located95. The ongoing challenge of disentangling the direct functional relationships
between the positions of genomic regions in the nucleus,
their local and long-​range contacts, and the state of gene
activity is being addressed by analysing chromatin contacts at the single-​cell level and with allele specificity,
for example, using DNA-​FISH21 or single-​cell Hi-​C73,74.
TADs and loop domains. At smaller scales, chromosomes fold into self-​associating chromatin domains,
termed TADs24,94,161 (Fig. 1). Chromatin domains had
been previously identified by microscopy but their
detailed genomic composition was unclear. Since the
discovery of TADs, the segmentation of the genome into
megabase-​sized domains has been extensively studied
in several organisms and with different methodologies,
leading to major breakthroughs in the discovery of
mechanisms of disease caused by congenital genomic
rearrangements3,47,162,163. TADs often enclose clusters of
co-​regulated enhancers and promoters164,165. Their size
has been re-​examined with the increasing resolution
afforded by improved 3C-​based assays, and found to
vary from 40 kb to 3 Mb in the human genome64, leading to the proposal of smaller loop domains as a substructure of TADs. Loop domains had been detected by
microscopy before the emergence of 3C-​technologies as
DNA loops between transcriptionally active regions166.
Loop domains derived from 3C-​based technologies
often coincide with pairs of convergent CTCF binding sites, indicating that CTCF binding can contribute
to the partition of specific regions of the genome into
self-​associating domains64,167–169. Higher-​order contacts
between TADs have also been investigated, leading to
the identification of metaTADs, which bring together
distant TADs in cell type-​specific patterns that relate to
gene activity154,170.
It has been debated whether TADs represent domains
that exist predominantly across the cell population or
represent an average of individual preferred contacts.
Although interactions observed in single cells by single-​
cell Hi-​C and imaging do not often identify whole
TADs, the contacts detected frequently occur with the
TAD coordinates defined by population Hi-​C32,34,72.
However, this preference might not be as strong as anticipated. Imaging of chromatin contacts in mouse ESCs
and oocytes showed that in 40% of cases 3D physical
distances between regions that flank TAD borders are
shorter than distances between regions within TADs73,
leading to highly variable contact clusters in individual
cells that do not coincide with the positions of TADs
in the cell population. This observation agrees with the
detection of chromatin contacts between regions separated by TAD borders in single cells, often found at
similar frequencies to regions within TADs21. However,
it is particularly noteworthy that combining the single-​
cell Hi-​C data results in the same TAD coordinates
observed in bulk population Hi-​C, which supports the
idea that TADs represent contact preferences of a cell
population, rather than compact domains of chromatin
in single cells73,171.
Chromatin contacts between cis-​regulatory elements.
Physical contacts between enhancers and promoters are
essential for the transcription of genes85 and can occur
over distances ranging from less than 1 kb up to several
megabases172–176 (Fig. 1). Genome-​wide maps of candidate
promoter–enhancer contacts can be created using high-​
resolution 3C-​based methodologies that enrich for contacts mediated by Pol II or promoter histone marks, or
that preferentially capture promoter-​based contacts52,80,81.
Direct pairwise contacts between gene promoters and
enhancers have become the most prominent concept of
enhancer function, possibly as a result of the increased
power of 3C-​based technologies to detect local pairwise contacts rather than higher-​order conformations.
However, other mechanisms for regulating enhancer
function are also emerging, which can involve formation of chromatin hubs, tethering of genes to active
chromatin or nuclear environments156,158,177,178 and phase
separation179,180. An interesting study in budding yeast
suggests homologue pairing as a mechanism for gene
activation181. In the diploid yeast genome, upon glucose
deprivation of the cell, both copies of the genomic locus
containing the gene TDA1 are relocalized to the nuclear
periphery, where the homologues associate with each
other and TDA1 expression is activated. A more classical concept of gene regulation can be observed at developmental loci, where cis-​regulatory contacts between
enhancers and promoters are thought to occur most
commonly within TADs48,162,182. Although regulatory
landscapes within TADs seem to be a common mecha­
nism, genes themselves also contact each other across
TAD boundaries over large genomic distances10,152,153,183.
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The authors thank the Helmholtz Association (Germany) for
support, C. Thieme (our laboratory) for help plotting GAM
and SPRITE contact maps in Figs 1 and 5a,b, and
S. Quinodoz (M. Guttman laboratory) for sharing SPRITE
contact cluster data (Figs 1 and 5b). A.P. acknowledges support from the National Institutes of Health Common Fund 4D
Nucleome Program grant U54DK107977. The authors apologize to the many scientists whose studies were not discussed
in our review due to length constraints.
Author contributions
All authors contributed to all aspects of the article.
Competing interests
A.P. has filed a patent application on GAM: Pombo, A.,
Edwards, P. A. W., Nicodemi, M., Beagrie, R. A. & Scialdone, A.
Patent application on ‘Genome Architecture Mapping’. Patent
PCT/EP2015/079413 (2015).
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